What makes a good first pilot

We see the same pattern repeatedly: organizations launch AI pilots with great fanfare, invest significant time and budget, then quietly shelve them when results don't materialize. The problem isn't usually the technology—it's pilot selection.

A good first pilot meets all five criteria. Miss one, and you're significantly more likely to fail.

Someone must personally care about the outcome. Not just "supportive" or "aligned"—genuinely invested in making it work. This person will fight for resources, remove obstacles, and champion the results. Without this, your pilot becomes an IT project that everyone tolerates but nobody truly wants to succeed.

You must be able to quantify success in business terms. "Faster processing" isn't enough—how much faster? "Better accuracy" isn't enough—better than what baseline? Good measures: "Reduce invoice processing time from 4 hours to 1 hour" or "Achieve 85% first-call resolution rate, up from current 67%."

AI needs data to learn from. If your data is scattered across systems, inconsistently formatted, or requires months of cleaning, choose a different use case for your first pilot. Look for processes where data already exists in a usable format. Customer support tickets, sales records, or operational logs often work well.

If the pilot fails or produces wrong answers, what happens? For a first pilot, the answer should be "not much." Avoid use cases where errors could damage customer relationships or regulatory compliance. Internal processes, non-critical forecasting, or content classification make safer starting points than customer-facing recommendations or financial decisions.

You should be able to measure progress weekly, not quarterly. Pilots that take 6 months to show results lose momentum and stakeholder interest. Choose use cases where you can demonstrate value within 4-8 weeks of starting development.

A logistics company wanted to automate invoice processing. Their accounting team spent 20+ hours weekly manually categorizing invoices by type and vendor. They had: - Willing owner: CFO personally frustrated by processing delays - Measurable value: Current 20 hours weekly processing time - Clean data: 18 months of consistently formatted invoices - Contained risk: Misclassification just meant manual review - Quick feedback: Results visible within days The pilot achieved 89% accuracy within 3 weeks, saving 17 hours of manual work weekly. More importantly, it gave the organization confidence to tackle larger AI challenges.

Good first pilots often seem "boring" compared to ambitious AI visions. Document processing, basic customer service routing, simple forecasting—these aren't exciting, but they work. The goal isn't to revolutionize your business with the first pilot. It's to build organizational capability and confidence for bigger challenges later.

A successful first pilot does more than solve one problem. It creates believers, builds internal AI expertise, and establishes processes for future projects. It answers the question "Can AI actually work here?" with a definitive yes. Choose wisely. Your first pilot shapes your organization's entire AI journey.